共查询到16条相似文献，搜索用时 234 毫秒
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结合经验模态分解（empiricalmodedecomposition,EMD）算法和自适应神经模糊推理系统（adap-tiveneuralfuzzyinferencesystem,ANFIS）算法应用于股票市场预测,提出了一种新的股票市场的预测模型,即EMD-ANFIS的多步预测模型。首先应用EMD算法把原始数据分解成不同尺度的基本模态函数（IMF）和残差（RES）,然后通过ANFIS算法对生成的各个IMF和RES进行自适应神经模糊推理,再把各个预测结果进行简单的聚合作为股票的预测价格,并与传统的预测方法进行比较,实验证明了EMD-ANFIS的多步预测模型具有更高的预测精度。 相似文献
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论文提出一种基于量子行为粒子群算法优化自适应模糊推理系统模型(ANFIS)参数,与之前使用梯度下降方法(Gradient Decent Method)不同,论文使用QPSO方法来训练ANFIS模型中隶属度函数的参数.经过训练后的ANFIS模型可以应用到非线性系统模型和混沌时序的预测.通过几组仿真实验结果表明基于量子粒子群方法训练ANFIS模型要优于基于粒子群算法方法训练ANFIS模型. 相似文献
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基于自适应模糊神经网络的可持续发展预测控制模型 总被引：1，自引：0，他引：1
生态经济系统是经济子系统、社会子系统、生态环境子系统相耦合的复杂巨系统。本文以模糊推理系统和BP神经网络相结合的自适应神经模糊推理系统(ANFIS)来模拟生态经济系统的功能，进行预测。通过确定7个指标，采集1991年-2003年的数据，构建模型，进行训练、检验，建立了以上海市为代表的基于ANFIS的可持续发展预测控制模型。 相似文献
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模糊规则提取和隶属度函数学习是模糊推理系统设计过程中重要而困难的问题。针对该问题,提出一种基于人工蜂群算法（ABC算法）训练自适应神经模糊推理系统（ANFIS）的新方法。神经网络采用5层ANFIS网络结构,并且描述了基本思想和算法实现过程。在ANFIS中引入ABC算法进行参数训练和优化,该方法适用于非线性系统辨识。实验结果表明,加入ABC算法之后,ANFIS训练和参数优化等取得了良好效果。 相似文献
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基于不同温度和大麦种群不同生长阶段（软组织、叶片发育阶段等）的麦蚜种群实验数据,首先利用自适应神经模糊推理系统（ ANFIS）,分别建立高斯型、三角型和梯型隶属度函数的麦蚜种群内禀增长率的T-S初始模糊模型。然后在误差允许范围内,应用ANFIS具体训练流程对相应初始模型进行修正,并通过误差选择与原始数据拟合程度最好的模型,最后利用数值模拟验证结论的正确性。 相似文献
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针对作物叶水势的连续测量问题,提出一种软测量建模新方法。该方法结合土壤-植物-大气连续体理论,选取8个易于获取的作物微环境信息作为辅助变量,利用自适应神经模糊推理系统(ANFIS)实现对作物叶水势的软测量建模。通过对实验数据的Matlab仿真表明,该模型方法简单有效,具有较高的软估计精度。 相似文献
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个人信用作为社会信用体系建设的重要部分,将其结合现代计算机理论技术来构建个人信用评分模型一直是研究的热点。本文利用前人遗传算法筛选出来的个人信用相关重要属性,并从这些重要属性的3种分类中依类定性地取出部分属性,结合自适应神经模糊推理系统理论（ANFIS）,建立基于遗传算法和AN-FIS的个人信用评分模型。对选取的数据实证分析,并与GA-SVM方法的结果作了比较,试验结果表明该模型只需少量重要属性变量就能够有较好的分类效果。 相似文献
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在分析模糊神经网络的模型、系统结构和学习算法的基础上,提出了基于自适应神经模糊推理系统(ANFIS)的自适应噪声抵消算法,并对算法进行了仿真和分析.仿真结果表明,在合理选取隶属度函数类型及其数目的条件下,ANFIS能够根据训练样本对隶属度函数参数等系统参数进行优化设计,从而大大提高模糊滤波输出的信噪比. 相似文献
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建立高精度水量预测算法模型,有利于水资源充分利用。以北京市2002-2015年需水量为例,对数据进行相关性分析后选出主要影响因素,然后采用主成分回归法、逐步回归法、灰色模型以及BP神经网络共4种方法进行建模,并用北京市2016年和2017年数据进行模型精度验证。结果表明：4种方法都适合用于城市需水量预测,其中主成分分析和逐步回归分析两种方法主要考虑了多元线性回归存在多重共线性,但是逐步回归模型优于主成分回归模型。将4种模型进行对比验证,BP神经网络模型预测精度最高,平均相对误差达到0.79%,用来预测2016-2017年需水量,预测结果分别为38.66亿m3、39.49亿m3,适合作为城市需水量预测方法。 相似文献
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INTRODUCTION Consumers’ acceptance of fresh or processedapples is the ultimate goal of apple breeders, foodscientists and supermarket managers. Internal qualityassessment has focused on two major objectives:removal of fruit with internal defects and taste selec-tion. Three major parameters including sugar content,acidity and firmness have to be taken into account todetermine the internal quality and the taste of an apple.Near infrared spectroscopy has been used to measureseveral properti… 相似文献
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To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r^2) of 0.759,low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way. 相似文献
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针对工业生产过程中的时变性问题,提出贝叶斯网络框架下的自适应质量变量预测建模方法。采用改进的即时学习策略,将数据库分成若干局部数据子集,快速选择与待测样本相似度较高的一组数据作为训练样本, 再利用主成分分析对训练样本过程变量进行特征提取,借此作为网络模型输入变量。利用基于改进Figueiredo-Jain算法的EM算法估计高斯混合模型参数,构建高斯混合模型逼近贝叶斯网络联合概率密度,训练得到贝叶斯网络下的自适应质量变量预测模型。基于田纳西伊斯曼（TE）仿真过程获得的数据,利用该方法对成分XG进行预测并与传统PCA-BN模型对比。结果证实该方法最大误差下降14.4%,均方根误差下降7.5%,相对误差下降8.3%,验证了该方法解决时变性问题的有效性。 相似文献
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Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results. 相似文献
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In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October. 相似文献
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In this paper, an integrated validation method and process are developed for multivariate dynamic systems. The principal component
analysis approach is used to address multivariate correlation and dimensionality reduction, the dynamic time warping and correlation
coefficient are used for error assessment, and the subject matter experts (SMEs)’ opinions and principal component analysis
coefficients are incorporated to provide the overall rating of the dynamic system. The proposed method and process are successfully
demonstrated through a vehicle dynamic system problem. 相似文献